Enterprise level software, especially middleware, has tens to hundreds of configuration parameters. As one example, the DB2 Universal Database Server from IBM Corporation (Armonk, N.Y.) has approximately 40 performance related configuration parameters (e.g., bufferpool sizes, time delay for writing commits, maximum number of database applications). The problem of configuring and tuning these parameters compounds when operating systems and additional middleware products like HyperText Transfer Protocol (HTTP) Servers and Web Applications Servers are needed to provide a complete system.
These challenges are well recognized, as evidenced by efforts to address them such as IBM Corporation's autonomic computing initiative that is developing self-managing systems. In particular, addressing self-configuration and self-optimization often depends on subtleties in the workload and system configuration.
This motivates the need for a generic approach that discovers the performance impact of configuration parameters by interacting with a target system.